• Journal of Information Technology Services
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• v.14 no.1
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• pp.145-158
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• 2015

Currently, Social Commerces have increasingly gained popularity with the growth of Social Network Services (SNS). As the applications of smartphones are being applied in widespread areas, social commerces in the smartphone environment have entered a new chapter. The applications of social commerces on smartphones are widely used, which has increased the market share of social commerces exponentially. Thus, we tried to find out factors which may affect the user acceptance of social commerces in the smartphone environment. We develop a research model to examine how social commerces in the smartphone environment are accepted by users based on the academic factors-switch costs, trend-seeking tendency, richness in media. The theoretical model is validated through an survey of social commerce users in the smartphone environment from the undergraduates and the graduates in Seoul, Korea. The structural equation analysis is conducted based on the partial least square (PLS) approach. The results reveal that the switch cost will have positive mediating influences to the intention to use social commerce in the smartphone environment. We also find that the perceived usefulness of the smartphone is affected by the media richness. The results also suggest that the trend-seeking tendency has no influences to the users of social commerces in the smartphone environment. Also, theoretical and practical implications are discussed. The findings are believed to increase our understanding an interesting mobile phenomenon, as well as making contributions.

• Korean Journal of Social Welfare
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• v.68 no.1
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• pp.73-94
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• 2016

The purpose of this study is to examine the trend of the studies on Korean social welfare administration over a recent 10 year period. Employing both network analysis and content analysis, which are representative statistical strategies to identify research trends, we analyzed 221 articles published in the Journal of Korean Social Welfare Administration from 2005 to 2014. The major findings were as follows; First, we found two clusters -"social (welfare) service" and "social welfare organization"- in the studies of social welfare administration. In addition, more than 80% of articles are mainly related with human resource management, including job satisfaction, organizational commitment, and so forth. Second, the newly emerging academic subjects such as social enterprise and community network appeared to be done independently of traditional subjects. Third, the proportion of quantitative studies being focused on human resources was overwhelmingly high compared to the other types of studies; therefore, there are a few studies using qualitative or mixed method, evidence based practice, and discourse studies. In addition, the studies of the rural areas, which are a blind spot of the social service delivery system, and the studies about information management, financial management, marketing, organization innovation rarely appeared, despite their significance.

• The Journal of Information Systems
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• v.26 no.4
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• pp.327-353
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• 2017

Purpose Social media such as social network services, online forums, and customer reviews have produced a plethora amount of information online. Yet, the information deluge has created both opportunities and challenges at the same time. This research particularly focuses on the challenges in order to discover and track the service defects over time derived by mining publicly available online customer reviews. Design/methodology/approach Synthesizing the streams of research from text analytics, we apply two stages of methods of sentiment analysis and structural topic model incorporating meta-information buried in review texts into the topics. Findings As a result, our study reveals that the research framework effectively leverages textual information to detect, prioritize, and categorize service defects by considering the moving trend over time. Our approach also highlights several implications theoretically and practically of how methods in computational linguistics can offer enriched insights by leveraging the online medium.

• Journal of Information Technology Services
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• v.13 no.4
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• pp.205-230
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• 2014

In recent years, the social network service (SNS) and the location-based social network service (LBSNS) industry is expanding and the competition within the field is increasing much more. Since 2010, the full-scaled studies of SNS and LBSNS have begun. With the growth of SNS and LBSNS markets, SoLoMo (Social-Local-Mobile) is also becoming the trend for applications in different fields. However, despite the importance of SoLoMo, there have been little studies on the characteristics of SoLoMo applications. The purpose of this research is to investigate the effect of characteristics and perceived privacy risk of mobile location-based SNS on intention to use SoLoMo applications. For the purpose, we proposed a SoLoMo service acceptance model with TAM (Technology Acceptance Model) and the characteristics of SoLoMo applications. The characteristics consist of three factors, i.e. SNS, location, and mobile-related factors. This study also considered a gamification and a perceived privacy risk factor influencing on SoLoMo service usage in our proposed research model. The results of our empirical analysis using partial least squares (PLS) method show that the characteristics of SoLoMo applications including SNS, location, and mobile-related features, gamification, and perceived privacy risk have partially an effect on intention to use SoLoMo applications. Based on these results, SoLoMo-related companies will be able to increase the usage of SoLoMo services by differentiating their own strategies with these factors influencing on SoLoMo services.

• Journal of Ocean Engineering and Technology
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• v.34 no.6
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• pp.461-468
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• 2020

This study examined how national research and development (R&D) in the domain of logistics has changed recently in the Republic of Korea. We conducted basic statistical analysis and social network analysis on 5,327 logistics-related R&D projects undertaken during 2005-2019. Data for performing these analyses were collected from the R&D database of the National Science and Technology Information Service (NTIS). By constructing a co-occurrence matrix with keywords, we conducted degree and betweenness centrality analysis and visualized the network matrix to display a cluster map. This study presents our observations related to the following findings: (1) the chronical trends of logistics R&D, (2) focused fields of logistics R&D, (3) the relations among keywords, and (4) the characteristics of logistics R&D. Finally, we suggest policy implications to boost and diversify logistics R&D.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.183-188
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• 2022

Educators have been making strides in the research into and practices supporting the policy of mainstreaming students with disability. A move towards including students with intellectual disability in community schools with all the other students can be seen in many countries' education systems, including that of the Kingdom of Saudi Arabia. The 'rights of the child' has been the main argument put forward by advocates of this policy in an attempt to move from the medical to the social model. This study argues that, although mainstreaming can be viewed as a positive trend toward effective education, its implementation remains somewhat problematic. It is believed that more investigative research into professionals' attitudes is needed to improve service provision and inform the administration of mainstreaming practices. The attitudes of special education teachers on the policy of mainstreaming are examined and emerging key themes discussed. Furthermore, challenges that continue to inhibit mainstreaming practices in Saudi Arabia are identified.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.163-170
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• 2021

As a big data is being used in various industries, big data market is expanding from hardware to infrastructure software to service software. Especially it is expanding into a huge platform market that provides applications for holistic and intuitive visualizations such as big data meaning interpretation understandability, and analysis results. Demand for big data extraction and analysis using social media such as SNS is very active not only for companies but also for individuals. However despite such high demand for the collection and analysis of social media data for user trend analysis and marketing, there is a lack of research to address the difficulty of dynamic interlocking and the complexity of building and operating software platforms due to the heterogeneity of various social media service interfaces. In this paper, we propose a method for developing a framework to operate the process from collection to extraction and classification of social media data. The proposed framework solves the problem of heterogeneous social media data collection channels through adapter patterns, and improves the accuracy of social topic extraction and classification through semantic association-based extraction techniques and topic association-based classification techniques.

• International Commerce and Information Review
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• v.19 no.1
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• pp.23-42
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• 2017

International Commerce and Information Review has been playing an important role of disseminating the outstanding research results in the fields such as trade information and systems, e-trade, regional studies, e-commerce, service trade, trade laws since 1999. This paper aims to find the research trends and distinguished characteristics in the field of trade information by analyzing research keywords of the research papers published in this journal using a social network analysis method. Research keyword data collected from the homepage of the academic society were cleaned and transformed into the co-occurrence network data, which are suitable for social network analysis. NodeXL Pro was used to analyze and visualize the pre-processed data. Through clustering analysis, the most important subject fields or interests were identified as well as those which worked as intermediaries for interdisciplinary researches.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.195-211
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• 2014

Cloud computing services provide IT resources as services on demand. This is considered a key concept, which will lead a shift from an ownership-based paradigm to a new pay-for-use paradigm, which can reduce the fixed cost for IT resources, and improve flexibility and scalability. As IT services, cloud services have evolved from early similar computing concepts such as network computing, utility computing, server-based computing, and grid computing. So research into cloud computing is highly related to and combined with various relevant computing research areas. To seek promising research issues and topics in cloud computing, it is necessary to understand the research trends in cloud computing more comprehensively. In this study, we collect bibliographic information and citation information for cloud computing related research papers published in major international journals from 1994 to 2012, and analyzes macroscopic trends and network changes to citation relationships among papers and the co-occurrence relationships of key words by utilizing social network analysis measures. Through the analysis, we can identify the relationships and connections among research topics in cloud computing related areas, and highlight new potential research topics. In addition, we visualize dynamic changes of research topics relating to cloud computing using a proposed cloud computing "research trend map." A research trend map visualizes positions of research topics in two-dimensional space. Frequencies of key words (X-axis) and the rates of increase in the degree centrality of key words (Y-axis) are used as the two dimensions of the research trend map. Based on the values of the two dimensions, the two dimensional space of a research map is divided into four areas: maturation, growth, promising, and decline. An area with high keyword frequency, but low rates of increase of degree centrality is defined as a mature technology area; the area where both keyword frequency and the increase rate of degree centrality are high is defined as a growth technology area; the area where the keyword frequency is low, but the rate of increase in the degree centrality is high is defined as a promising technology area; and the area where both keyword frequency and the rate of degree centrality are low is defined as a declining technology area. Based on this method, cloud computing research trend maps make it possible to easily grasp the main research trends in cloud computing, and to explain the evolution of research topics. According to the results of an analysis of citation relationships, research papers on security, distributed processing, and optical networking for cloud computing are on the top based on the page-rank measure. From the analysis of key words in research papers, cloud computing and grid computing showed high centrality in 2009, and key words dealing with main elemental technologies such as data outsourcing, error detection methods, and infrastructure construction showed high centrality in 2010~2011. In 2012, security, virtualization, and resource management showed high centrality. Moreover, it was found that the interest in the technical issues of cloud computing increases gradually. From annual cloud computing research trend maps, it was verified that security is located in the promising area, virtualization has moved from the promising area to the growth area, and grid computing and distributed system has moved to the declining area. The study results indicate that distributed systems and grid computing received a lot of attention as similar computing paradigms in the early stage of cloud computing research. The early stage of cloud computing was a period focused on understanding and investigating cloud computing as an emergent technology, linking to relevant established computing concepts. After the early stage, security and virtualization technologies became main issues in cloud computing, which is reflected in the movement of security and virtualization technologies from the promising area to the growth area in the cloud computing research trend maps. Moreover, this study revealed that current research in cloud computing has rapidly transferred from a focus on technical issues to for a focus on application issues, such as SLAs (Service Level Agreements).